Page 113 - Read Online
P. 113

Niu et al. J. Mater. Inf. 2025, 5, 45  https://dx.doi.org/10.20517/jmi.2025.22  Page 15 of 15

               23.      Moret, M.; Friedrich, L.; Grisoni, F.; Merk, D.; Schneider, G. Generative molecular design in low data regimes. Nat. Mach. Intell.
                   2020, 2, 171-80.  DOI
               24.      Sumita, M.; Terayama, K.; Suzuki, N.; et al. De novo creation of a naked eye-detectable fluorescent molecule based on quantum
                   chemical computation and machine learning. Sci. Adv. 2022, 8, eabj3906.  DOI  PubMed  PMC
               25.      Joung, J. F.; Han, M.; Jeong, M.; Park, S. Experimental database of optical properties of organic compounds. Sci. Data. 2020, 7, 295.
                   DOI  PubMed  PMC
               26.      Gong, J.; Gong, W.; Wu, B.; et al. ASBase: the universal database for aggregate science. Aggregate 2023, 4, e263.  DOI
               27.      Li, P.; Wang, Z.; Li, W.; Yuan, J.; Chen, R. Design of thermally activated delayed fluorescence materials with high intersystem
                   crossing efficiencies by machine learning-assisted virtual screening. J. Phys. Chem. Lett. 2022, 13, 9910-8.  DOI
               28.      Kim, H.; Lee, K.; Kim, J. H.; Kim, W. Y. Deep learning-based chemical similarity for accelerated organic light-emitting diode
                   materials discovery. J. Chem. Inf. Model. 2024, 64, 677-89.  DOI
               29.      Blaskovits, J. T.; Laplaza, R.; Vela, S.; Corminboeuf, C. Data-driven discovery of organic electronic materials enabled by hybrid top-
                   down/bottom-up design. Adv. Mater. 2024, 36, e2305602.  DOI  PubMed
               30.      Zdrazil, B.; Felix, E.; Hunter, F.; et al. The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data
                   types and time periods. Nucleic. Acids. Res. 2024, 52, D1180-92.  DOI  PubMed  PMC
               31.      Guo, J.; Sun, M.; Zhao, X.; et al. General graph neural network-based model to accurately predict cocrystal density and insight from
                   data quality and feature representation. J. Chem. Inf. Model. 2023, 63, 1143-56.  DOI
               32.      Medina-Franco, J. L.; Martínez-Mayorga, K.; Bender, A.; Scior, T. Scaffold diversity analysis of compound data sets using an entropy-
                   based measure. QSAR. Comb. Sci. 2009, 28, 1551-60.  DOI
               33.      Niu, Y.; Li, W.; Peng, Q.; et al. MOlecular MAterials Property Prediction Package (MOMAP) 1.0: a software package for predicting
                   the luminescent properties and mobility of organic functional materials. Mol. Phys. 2018, 116, 1078-90.  DOI
               34.      Park, Y.; Lee, J.; Jung, D. H.; et al. An aromatic imine group enhances the EL efficiency and carrier transport properties of highly
                   efficient blue emitter for OLEDs. J. Mater. Chem. 2010, 20, 5930.  DOI
               35.      Kotaka, H.; Konishi, G.; Mizuno, K. Synthesis and photoluminescence properties of π-extended fluorene derivatives: the first example
                   of a fluorescent solvatochromic nitro-group-containing dye with a high fluorescence quantum yield. Tetrahedron. Lett. 2010, 51, 181-
                   4.  DOI
               36.      Liu, X.; Liang, F.; Ding, L.; et al. The study on two kinds of spiro systems for improving the performance of host materials in blue
                   phosphorescent organic light-emitting diodes. J. Mater. Chem. C. 2015, 3, 9053-6.  DOI
   108   109   110   111   112   113   114   115   116   117   118